Advances in Reliability and Statistical Computing for Intelligent Systems

Editors

• Hoang Pham, Rutgers, The State University of New Jersey

Description

The era of AI, through its focus on the reliability and statistical machine computing of intelligent systems in everyday applications and the service industry, has experienced a dramatic shift in recent years. Such systems require reliable and timely responses.

Articles concerning new theoretical research and methods in advanced reliability and statistical computing for intelligent systems are solicited. Preference will be given to papers with real-world applications over purely theoretical papers.

Potential topics

• Mathematical reliability and statistical methods
• Big data modeling and prediction
• Statistical learning algorithms, models, and theories
• Machine learning models for intelligent systems
• Text mining and deep machine learning
• Intelligent system dependability and performability
• Reliability modeling and optimization
• High-dimensional data analysis
• Statistical inference for intelligent systems
• Industrial case studies in intelligent systems, including field and service robotics, medical care, education, visual surveillance, intelligent transportation, etc.